Content scheduling method and apparatus

ABSTRACT

A content scheduling method is provided. The content scheduling method, which is performed by a content scheduling apparatus, comprises acquiring a total play count of target content, determining a plurality of weight values of the target content with respect to a plurality of time slots, each weight value of the plurality of weight values indicating a first preference for the target content with respect to each time slot of the plurality of time slots, generating a linear programming model using the acquired total play count and the plurality of weight values and determining, via a processor, a play count of the target content in the each time slot of the plurality of time slots based on the linear programming model.

This application claims priority to Korean Patent Application No.10-2016-0134105, filed on Oct. 17, 2016, and all the benefits accruingtherefrom under 35 U.S.C. § 119, the disclosure of which is incorporatedherein by reference in its entirety.

BACKGROUND 1. Field

The present disclosure relates to a content scheduling method andapparatus, and more particularly, to a method and apparatus forscheduling content in units of time zones so as to improve the exposureeffect of the content.

2. Description of the Related Art

With the development of display device and network technologies, outdooradvertising utilizing various forms of digital signages such as videowalls and kiosks is becoming a new trend in the advertisement industry.

In outdoor advertising using a digital signage, content scheduling isindispensable to improve the exposure of advertising to targetcustomers. This is because, in order to improve the exposure effect ofadvertisement content to its target customer base that varies from hourto hour, the advertisement content needs to be appropriately assignedbetween time zones.

FIGS. 1A and 1B are graphs showing an improvement in the effect ofexposure of advertising to a target customer base that can be achievedby content scheduling. Referring to FIGS. 1A and 1B, even if the totalplay count of advertisement content is uniform, the effect of exposureof the advertisement content can be considerably improved if theadvertisement content is scheduled in consideration of a total floatingpopulation in each time zone.

However, due to lack of a scheduling algorithm, an existing outdooradvertising system using a digital signage has not yet been able toperform content scheduling in consideration of the number of targetcustomers. In addition, in the case of outdoor advertising using adigital signage, various constraints such as different play rates fordifferent types of advertisement content are imposed, thereby makingcontent scheduling highly difficult and complicated.

Accordingly, a content scheduling method is needed which is capable ofsatisfying various constraints that can be imposed in an outdooradvertisement system and maximizing the effect of exposure ofadvertisement content to its target customer base.

SUMMARY

Exemplary embodiments of the present disclosure provide a contentscheduling method and apparatus capable of improving the effect ofexposure of content to a target customer base.

Exemplary embodiments of the present disclosure also provide a linearprogramming model capable of maximizing the effect of exposure ofcontent to a target customer base while satisfying various constraintsimposed.

However, exemplary embodiments of the present disclosure are notrestricted to those set forth herein. The above and other exemplaryembodiments of the present disclosure will become more apparent to oneof ordinary skill in the art to which the present disclosure pertains byreferencing the detailed description of the present disclosure givenbelow.

According to an exemplary embodiment of the present disclosure, there isprovided a content scheduling method, which is performed by a contentscheduling apparatus, comprising acquiring a total play count of targetcontent, determining a plurality of weight values of the target contentwith respect to a plurality of time slots, each weight value of theplurality of weight values indicating a first preference for the targetcontent with respect to each time slot of the plurality of time slots,generating a linear programming model using the acquired total playcount and the plurality of weight values and determining, via aprocessor, a play count of the target content in the each time slot ofthe plurality of time slots based on the linear programming model.

According to another exemplary embodiment of the present disclosure,there is provided a content scheduling apparatus comprising at least oneprocessor, a network interface configured to communicate with a contentplaying apparatus, a memory configured to load a computer programexecuted by the at least one processor and a storage configured to storethe computer program which, when executed by the at least one processor,causes the at least one processor to perform operations comprisingacquiring a total play count of target content, determining a pluralityof weight values of the target content with respect to a plurality oftime slots, each weight value of the plurality of weight valuesindicating a preference for the target content with respect to each timeslot of the plurality of time slots, generating a linear programmingmodel using the acquired total play count and the plurality of weightvalues and determining a play count of the target content in the eachtime slot of the plurality of time slots based on the linear programmingmodel.

According to another exemplary embodiment of the present disclosure,there is provided a computer-readable storage medium storinginstructions which, when executed by a processor, cause the processor toperform operations comprising acquiring a total play count of targetcontent, determining a plurality of weight values of the target contentin with respect to a plurality of time slots, each weight value of theplurality of weight values indicating a preference for the targetcontent with respect to each time slot of the plurality of time slots,generating a linear programming model using the acquired total playcount and the plurality of weight values and determining a play count ofthe target content in the each time slot of the plurality of time slotsbased on the linear programming model.

According to the aforementioned and other exemplary embodiments of thepresent disclosure, the effect of exposure of target content to a targetcustomer base can be improved by scheduling the target content inconsideration of the target content's preference for each time zone. Forexample, if the target content is advertisement content, the effect ofexposure of the advertisement content to its target customer base can beimproved by scheduling the target content in a preferred time zone witha large floating population of target customers.

In addition, a content providing entity's profit can be increased byimproving the effect of exposure of target content to a target customerbase through scheduling. For example, if the target content isadvertisement content, the advertising entity's profit can be increasedby improving the effect of exposure of the target content to its targetcustomer base. Since the effect of exposure of the target content to itstarget customer base can be improved, the profit of advertising companycan be increased, and as a result, the profit of the operator of acontent playing apparatus (or a content playing entity) can also beincreased.

Moreover, since scheduling is performed in consideration of not onlytarget content's preference for each time zone, but also the targetcontent's priority value determined by the contract cost of the targetcontent, the profit of the content playing entity can be furtherincreased. For example, if there exists multiple target content itemsand the multiple target content items have the same or similar preferredtime zones, target content items having a high contract cost are may beprioritized over other target content items, and thus, the contract costfor securing preferred time zones for content may be raised. Therefore,the profit of the content playing entity can be further increased.

Other features and exemplary embodiments may be apparent from thefollowing detailed description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other exemplary embodiments and features of the presentdisclosure will become more apparent by describing in detail exemplaryembodiments thereof with reference to the attached drawings, in which:

FIGS. 1A and 1B are graphs showing differences in the effect of exposureof advertising that can be achieved by content scheduling;

FIG. 2 is a schematic view for explaining the content of contentscheduling according to an exemplary embodiment of the presentdisclosure;

FIG. 3 is a configuration diagram of a content providing systemaccording to an exemplary embodiment of the present disclosure;

FIG. 4 is a hardware configuration diagram of a content schedulingapparatus according to an exemplary embodiment of the presentdisclosure;

FIG. 5 is a functional block diagram of a content scheduling apparatusaccording to an exemplary embodiment of the present disclosure;

FIG. 6 is a flowchart illustrating a content scheduling method accordingto an exemplary embodiment of the present disclosure;

FIGS. 7A through 7C are views for explaining the relationship betweenthe length of each time slot and the play time of target content;

FIGS. 8A through 8C are views for explaining how to determine the weightvalue of target content in each time slot based on the target content'spreference for each target customer base and the floating population ofeach target customer base; and

FIGS. 9A through 13 are views for explaining linear programming modelsaccording to some exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION

Hereinafter, preferred embodiments of the present invention will bedescribed with reference to the attached drawings. Advantages andfeatures of the present invention and methods of accomplishing the samemay be understood more readily by reference to the following detaileddescription of preferred embodiments and the accompanying drawings. Thepresent invention may, however, be embodied in many different forms andshould not be construed as being limited to the embodiments set forthherein. Rather, these embodiments are provided so that this disclosurewill be thorough and complete and will fully convey the concept of theinvention to those skilled in the art, and the present invention willonly be defined by the appended claims. Like numbers refer to likeelements throughout.

Unless otherwise defined, all terms including technical and scientificterms used herein have the same meaning as commonly understood by one ofordinary skill in the art to which this invention belongs. Further, itwill be further understood that terms, such as those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art and thepresent disclosure, and will not be interpreted in an idealized oroverly formal sense unless expressly so defined herein. The terms usedherein are for the purpose of describing particular embodiments only andis not intended to be limiting. As used herein, the singular forms areintended to include the plural forms as well, unless the context clearlyindicates otherwise.

The terms “comprise”, “include”, “have”, etc. when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, components, and/or combinations of them but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or combinationsthereof. The definitions of terms used in this specification are asfollows.

As used herein, the term “scheduling” refers to a task of determiningthe play count or the play order of target content in each time slotduring a scheduling target period.

As used herein, the term “content” refers to digital information thatcan be played by a device having a display function, such as, forexample, advertisement content, movie content, music content, or thelike.

As used herein, the term “scheduling target period” refers to an entireperiod during which content scheduling is performed.

As used herein, the term “time slot” refers to a segment obtained bydividing a scheduling target period and also means the unit of time fordetermining the play count of target content. For example, when thescheduling target period is one day and the play count of the targetcontent is determined every one hour, a total of 24 time slots (=24 hr/1hr) are generated, and the play count of the target content in each timeslot is calculated and provided as a scheduling result. In this example,the length of each time slot may be one hour.

As used herein, the term “play time of content” refers to the runningtime or the length of content.

As used herein, the term “total play count” refers to a minimum playcount, which is a minimum number of times each content should be playedby a content playing apparatus 300, or a maximum play count, which is amaximum number of times each content can be played by the contentplaying apparatus 300. For example, the total play count ofadvertisement content may be a minimum number of plays of theadvertisement content designated by an advertising contract.

As used herein, the term “total floating population” (or the number ofthe floating population) refers to a number of people who visited orpass by a designated geographical area (e.g. near the area where acontent playing apparatus is installed).

Exemplary embodiments of the present disclosure will hereinafter bedescribed with reference to the accompanying drawings.

First, for a better understanding of the inventive concept of thepresent disclosure, the concept of content scheduling according to anexemplary embodiment of the present disclosure will hereinafter bedescribed with reference to FIG. 2. In the description that follows, itis assumed that the term “content” as in “content scheduling” denotesadvertisement content, unless specified otherwise. However, it isobvious that content scheduling is also applicable to various types ofcontent, other than advertisement content. In other words, the inventiveconcept of the present disclosure is also applicable to, for example,music content, video content, and the like.

Referring to FIG. 2, content scheduling may be understood as being anoperation of determining an optimal placement of target content in eachtime zone in order to achieve a particular objective, for example,maximize the effect of exposure of the target content to its targetcustomer base. If various constraints are added, as shown in FIG. 2,content scheduling determines an optimal placement of the target contentthat can satisfy the added constraints. The added constraints mayinclude the total play count of the target content that can bedetermined by a contact between a content providing entity and a contentplaying entity, the operation time of a content playing apparatus, andthe play ratio of public advertisement content mandated by a regulation.

In short, content scheduling is an operation of determining the optimalplacement of the target content in each time zone in order to achieve aparticular objective while satisfying the given constraints. If theparticular objective is represented as an objective function, contentscheduling may be understood as being an operation of determining aplacement of the target content in each time zone that can optimize(i.e., maximize or minimize) the value of the objective function.

The placement of the target content in each time zone is an operation offinding an optimal combination of the target content among a pluralityof combinations of the target content. and may thus be considered a typeof combination optimization problem. Thus, when the relationship betweenan objective function and a constraint are modeled as a linearrelationship, the placement of the target content in each time zone maybe determined using a linear programming model. That is, contentscheduling may be performed by finding the optimal solution orapproximate optimal solution of a linear programming model having theobjective function and the constraint.

A linear programming model is a model capable of finding an optimalsolution or an approximate optimal solution through linear programmingand includes a decision variable, an objective function, and aconstraint. The linear programming model is already well known in theart to which the present disclosure pertains, and thus, a furtherdescription thereof will be omitted.

Exemplary embodiments of the present disclosure, which embody theabove-described concept of content scheduling, will hereinafter bedescribed with reference to the accompanying drawings.

FIG. 3 is a configuration diagram of a content providing systemaccording to an exemplary embodiment of the present disclosure.

Referring to FIG. 3, the content providing system is a system thatproduces a scheduling result by calculating the play count of targetcontent in each time slot, and allocates the target content between timeslots and plays the target content according to the scheduling result.For example, the content providing system may be an outdoor advertisingsystem playing advertisement content scheduled via a digital signage.

The content providing system may include a content scheduling apparatus100 and at least one content playing apparatus 300. The contentscheduling apparatus 100 and the content playing apparatus 300 may beconnected via a network. Although not specifically illustrated in FIG.3, the content providing system may further include a contentcontrolling apparatus controlling the content playing apparatus 300according to the scheduling result provided by the content schedulingapparatus 100. Alternatively, the content scheduling apparatus 100 maybe configured to perform not only the functions of a content managingapparatus, but also the functions of a content controlling apparatus.

The content scheduling apparatus 100 may be a computing device capableof producing a scheduling result for the target content. If the contentscheduling apparatus 100 serves not only as a content managingapparatus, but also as a content controlling apparatus, the contentscheduling apparatus 100 may control the content playing apparatus 300via a network based on the scheduling result to control the play countand the play order of the target content. A method in which the contentscheduling apparatus 100 schedules the target content will be describedlater with reference to FIG. 6.

The computing device may be, for example, a notebook computer, a desktopcomputer, a laptop computer, or the like, but the present disclosure isnot limited thereto. That is, examples of the computing device includenearly all types of devices having a computing function and acommunication function.

The content playing apparatus 300 is a device for playing contentaccording to the scheduling result provided by the content schedulingapparatus 100. The content playing apparatus 300 may be implemented asvarious forms of digital signages such as, for example, a kiosk or avideo wall installed in a public place (such as a bus terminal) or acommercial place (such as a shopping mall), but the present disclosureis not limited thereto. That is, the content playing apparatus 300 maybe implemented as various types of devices having a content playerfunction and a communication function. In a case where the contentplaying apparatus 300 is configured to play music content, the contentplaying apparatus 300 may not be provided with a display screen.

The network that connects the content scheduling apparatus 100 and thecontent playing apparatus 300 may be implemented as any type ofwired/wireless network such as, for example, a local area network (LAN),a wide area network (WAN), or a mobile radio communication network.

FIG. 3 illustrates the content scheduling apparatus 100 and the contentplaying apparatus 300 as being separate physical devices, but thepresent disclosure is not limited thereto. That is alternatively, thecontent scheduling apparatus 100 and the content playing apparatus 300may be implemented as different logics provided in the same physicaldevice.

The content providing system according to an exemplary embodiment of thepresent disclosure has been described above with reference to FIG. 3.The detailed structure and the operation of the content schedulingapparatus 100 will hereinafter be described with reference to FIGS. 4and 5.

Referring to FIG. 4, the content scheduling apparatus 100 may include atleast one processor 110, a network interface 170, which performscommunication with the content playing apparatus 300, a memory 130,which loads a computer program executed by the processor 110, and astorage 190, which stores content scheduling software 191. FIG. 4illustrates only the elements that are closely relevant to exemplaryembodiments of the present disclosure, but obviously, the contentplaying apparatus 300 may further include various general-purposeelements other than those set forth in FIG. 4.

The processor 110 controls the general operations of the elements of thecontent scheduling apparatus 100. The processor 110 may be configured toinclude a central processing unit (CPU), a micro-processor unit (MPU), amicro-controller unit (MCU), or an arbitrary processor that is alreadywell known in the art to which the present disclosure pertains. Theprocessor 110 may execute at least one application or program to performa content scheduling method according to an exemplary embodiment of thepresent disclosure.

The memory 130 stores various data, instructions, and/or information.The memory 130 may load at least one program 191 from the storage 190 toperform the content scheduling method according to an exemplaryembodiment of the present disclosure. FIG. 4 shows a random accessmemory (RAM) as an example of the memory 130.

The bus 150 provides an inter-component communication function for thecontent scheduling apparatus 100. The bus 150 may be implemented asvarious types of buses such as an address bus, a data bus, and a controlbus.

The network interface 170 supports wired or wireless communication ofthe content scheduling apparatus 100. To this end, the network interface170 may include a communication module that is already well known in theart to which the present disclosure pertains.

The network interface 170 may exchange data with the content playingapparatus 300 of FIG. 3 via a network. Also, the network interface 170may transmit a control command to, or receive a control command from,the content playing apparatus 300 to perform content scheduling.

The storage 190 may non-temporarily store the program 191. FIG. 4 showscontent scheduling software 191 as an example of the program 191.

The storage 190 may be a nonvolatile memory such as a read only memory(ROM), an erasable programmable ROM (EPROM), an electrically erasableprogrammable (EEPROM), or a flash memory, a hard disk, a removable disk,or any arbitrary form of computer-readable recording medium that isalready well known in the art to which the present disclosure pertains.

The content scheduling software 191 is loaded in the memory 130 and isexecuted by the processor 110 to perform an operation 131 of acquiringthe total play count of target content, an operation 133 of determiningthe weight value of the target content in each time slot, whichindicates the target content's preference for each time slot, anoperation 135 of generating a linear programming model using the totalplay count of the target content and the weight value of each time slot,and an operation 137 of determining the play count of the target contentin each time slot using the linear programming model.

Referring to FIG. 5, the content scheduling apparatus 100 may include aninformation acquisition unit 210, a linear programming model generationunit 230, and an optimal solution calculation unit 250. FIG. 5illustrates only the elements that are closely relevant to exemplaryembodiments of the present disclosure, but obviously, the contentscheduling apparatus 100 may further include various general-purposeelements other than those set forth in FIG. 5. The functions of thecontent scheduling apparatus 100 may be performed by the processor 110of the content scheduling apparatus 100 of FIG. 4.

The information acquisition unit 210 may acquire various information forgenerating a linear programming model and may determine the weight valueof each time slot and the priority value of target content based on theacquired information. The acquired information may include, for example,the play time of the target content, the total play count of the targetcontent during a scheduling target period, a total floating populationin each time zone, the target content's preference for each targetcustomer base, and the floating population of each target customer basein each time zone.

The information acquisition unit 210 may determine the weight value ofeach time slot based on the target content's preference for each targetcustomer base and the floating population of each target customer basein each time slot.

A method in which the information acquisition unit 210 acquiresinformation may vary depending on how the content scheduling apparatus100 is implemented. For example, the information acquisition unit 210may receive information directly from a user via a graphical userinterface (GUI) or may acquire information (such as the total play countof the target content) from a previously-stored configuration file. Theinformation acquisition unit 210 may also acquire information from datareceived via a network. That is, the information acquisition unit 210may acquire information in various manners, depending on how the contentscheduling apparatus 100 is implemented.

The linear programming model generation unit 230 generates a linearprogramming model for performing scheduling based on the informationacquired by the information acquisition unit 210. Specifically, thelinear programming model generation unit 230 sets the play count of thetarget content in each time slot as a decision variable based on theacquired information, sets a function that maximizes the sum of theweight value of the target content in each time slot, multiplied by thedecision variable for each time slot, as an objective function, and setsthe total play count of the target content as a constraint. The decisionvariable, the objective function, and the constraint may vary dependingon the acquired information.

For reference, when the play count of the target content in each timeslot is set as the decision variable, the decision variable may be aninteger of zero or greater. Thus, the optimal solution or approximateoptimal solution of the linear programming model generated by the linearprogramming model generation unit 230 can be found according to aninteger linear programming method.

The optimal solution calculation unit 250 provides a scheduling resultby finding an optimal solution or an approximate optimal solutionsatisfying the linear programming model generated by the linearprogramming model generation unit 230. The optimal solution calculationunit 250 may use one or more algorithms that are already well known inthe art to which the present disclosure pertains to find the optimalsolution or approximate optimal solution of the generated linearprogramming model. For example, the optimal solution calculation unit250 may use a branch and bound method to find the optimal solution ofthe generated linear programming model or may use a simplex method toconvert the generated linear programming model into standard form tofind the approximate optimal solution of the generated linearprogramming model. The approximate optimal solution found by the simplexmethod may be converted into an integer through rounding up or down, andthe result of the conversion may be provided as the optimal solution ofthe generated linear programming model.

The optimal solution calculation unit 250 may include a software modulethat is already well known in the art to which the present disclosurepertains, such as Lindo or Lingo software, to perform the branch andbound method or the simplex method.

The elements of the content scheduling apparatus 100 of FIG. 5 may beeither software elements or hardware elements such as field programmablegate arrays (FPGAs) or application-specific integrated circuits (ASICs),but the present disclosure is not limited thereto. The elements of thecontent scheduling apparatus 100 of FIG. 5 may be configured to residein an addressable storage medium or to execute one or more processors.Each of the elements of the content scheduling apparatus 100 of FIG. 5may be divided into one or more sub-elements such that the functions ofthe corresponding element can be distributed between the sub-elements,or the elements of the content scheduling apparatus 100 of FIG. 5 andthe functions thereof may be incorporated into fewer elements.

The content scheduling apparatus 100 has been described above withreference to FIGS. 4 and 5. A content scheduling method according to anexemplary embodiment of the present disclosure will hereinafter bedescribed with reference to FIG. 6.

FIG. 6 is a flowchart illustrating a content scheduling method accordingto an exemplary embodiment of the present disclosure. For ease ofunderstanding, it is noted that an entity performing each step of thecontent scheduling method may be omitted in the description thatfollows.

Referring to FIG. 6, the content scheduling apparatus 100 acquires thetotal play count of target content (S100). As already mentioned above,the total play count of the target content refers to the minimum playcount of the target content, which is a minimum number of times thetarget content should be played during a scheduling target period or themaximum number of the target content, which is a maximum number of timesthe target content can be played during the scheduling target period.The total play count of the target content may be determined by, forexample, a contract between a content providing entity and a contentplaying entity.

Thereafter, the content scheduling apparatus 100 determines the weightvalue of the target content in each time slot, which indicates thetarget content's preference for each time slot (S300). For example, ifthe length of each time slot is “one hour”, the content schedulingapparatus 100 may determine a predetermined weight value every one hour.The weight value of each time slot is set as a coefficient of anobjective function of a linear programming model generated by thecontent scheduling apparatus 100, and may be determined based on variousinformation according to the objective of scheduling.

For example, in order to allocate the target content to a time zonehaving a large floating population, regardless of each target customerbase of the target content, the content scheduling apparatus 100 maydetermine the weight value of each time slot based on a total floatingpopulation in each time slot.

In another example, in a case where scheduling is performed to maximizethe effect of exposure of the target content to its target customer basewith any given contract cost, the content scheduling apparatus 100 maydetermine the weight value of each time slot based on a value obtainedby dividing the floating population of each target customer base in eachtime slot by the contract cost of the target content.

In still another example, in a case where scheduling is performed inorder to maximize the effect of exposure of the target content to itstarget customer base with any given contract cost, the contentscheduling apparatus 100 may determine the weight value of each timeslot based on the floating population of each target customer base ineach time slot. For example, if the target customer base of the targetcontent is males in their 20s, the content scheduling apparatus 100 maydetermine the weight value of each time slot based on the number ofmales in their 20s among the floating population in each time slot. Thatis, the content scheduling apparatus 100 may determine the weight valueof each time slot by assigning a greater weight value to a time slothaving a large floating population of target customers, i.e., a timeslot having a large population of males in their 20s, than to other timeslots.

Information such as the total floating population in each time slot andthe floating population of each target customer base in each time slotmay be acquired by video analytics. For example, in a case where thecontent playing apparatus 300 is a digital signage installed in ashopping mall, an image capturing apparatus installed near or embeddedin the digital signage may capture or collect images of the surroundingsof the digital signage, and an image analysis module may extractdemographic data such as the total floating population in each time zoneand the sexes and ages of members of the floating population in eachtime zone by applying a computer vision algorithm to the captured orcollected images. Also, the image analysis module may determine thefloating population of each target customer base in each time slot basedon the total floating population in each time zone and the demographicdata. Alternatively, the content scheduling apparatus 100 may acquirethe total floating population in each time zone and the demographic datafrom the image analysis module and may determine the floating populationof each target customer base in each time slot based on the totalfloating population in each time zone and the demographic data.

In order to precisely calculate the total floating population in eachtime slot, WiFi data collected via an access point (AP) may beadditionally used. For example, in a case where a user passes by thecontent playing apparatus 300, communication for establishing a WiFiconnection between the user's mobile terminal and the content playingapparatus 300 may be performed. Once a WiFi connection is establishedbetween the user's mobile terminal and the content playing apparatus300, a WiFi probe signal may be collected via the AP, and the totalfloating population in each time zone may be measured using traffic dataof the probe signal.

The content scheduling apparatus 100 may determine the weight value ofthe target content in each time slot based on the target content'spreference for each target customer base and the floating population ofeach target customer base in each time slot, and this will be describedlater with reference to FIGS. 8A through 8C.

Thereafter, the content scheduling apparatus 100 generates a linearprogramming model using the total play count of the target content andthe weight value of each time slot (S500). Specifically, the contentscheduling apparatus 100 sets the play count of the target content ineach time slot as a decision variable, sets an objective function usingthe weight value of each time slot and the decision variable, and sets aconstraint using the total play count of the target content and thedecision variable. The linear programming model may vary depending onthe type of information acquired in S100 and will be described laterwith reference to FIGS. 9A through 13.

Thereafter, the content scheduling apparatus 100 determines the playcount of the target content in each time slot using the linearprogramming model (S700). In other words, the content schedulingapparatus 100 may determine the play count of the target content in eachtime slot by calculating the value of the decision variable thatcorresponds to the optimal solution or the approximate optimal solutionof the linear programming model. As described above, the optimalsolution or the approximate optimal solution of the linear programmingmodel may be calculated using the branch and bound method or the simplexmethod.

In the content scheduling method according to the exemplary embodimentof FIG. 6, the content scheduling apparatus 100 can perform schedulingin consideration of the weight value of the target content in each timeslot, which indicates the target content's preference for each timeslot. Also, even if the objective of scheduling is changed, a flexiblescheduling can be performed simply by changing the weight value of eachtime slot. Also, the effect of exposure of the target content to itstarget customer base can be improved by determining the weight value ofeach time slot based on the total floating population in each time slot.

In the content scheduling method according to the exemplary embodimentof FIG. 6, a scheduling result is the play count of the target contentin each time slot. If the play time of the target content and the lengthof each time slot are identical, the play count of the target content ineach time slot may be zero or one. In this case, once the contentscheduling apparatus 100 determines the play count of the target contentin each time slot, the play order of the target content may beautomatically determined. This will hereinafter be described in detailwith reference to FIGS. 7A through 7C.

FIG. 7A shows the total play counts of content items A and B, FIG. 7Bshows a case where each time slot is longer than the play times of thecontent items A and B, and FIG. 7B shows a case where the length of eachtime slot and the play times of the content items A and B are identical.Tables 710 and 730 of FIGS. 7B and 7C show the weight values of thecontent items A and B in each time slot, and tables 720 and 740 of FIGS.7B and 7C show a scheduling result, i.e., the play counts of the contentitems A and B in each time slot. For convenience, it is assumed that theplay times of the content items A and B are identical.

Referring to FIG. 7A, the total play count of the content item A is “2”,and the total play count of the content item B is “4”.

Referring to FIG. 7B, by performing scheduling in consideration of theweight values of the content items A and B in each time slot, thecontent scheduling apparatus 100 determines that the play counts of thecontent items A and B in a first time slot are “2” and “1”,respectively. Since multiple content items each having a total playcount of 1 or greater, i.e., the content items A and B, are allocated tothe first time slot, an additional scheduling process may be needed todetermine the play order of the content items A and B.

Specifically, the content scheduling apparatus 100 may perform anadditional scheduling process to determine the play order of targetcontent. For example, for a plurality of target content items allocatedto a single time slot, the content scheduling apparatus 100 may performan additional scheduling process to arrange the plurality of targetcontent items either alternately or in a random order.

For content having a short play time, such as advertisement content,each time slot may preferably be set to be longer than the play time ofthe content, as shown in FIG. 7B, in order to reduce the cost ofcomputing needed by scheduling.

Referring to FIG. 7C, the content scheduling apparatus 100 allocates thecontent item A, which has a large weight value in first and fourth timeslots, to the first and fourth time slots. Specifically, the contentscheduling apparatus 100 determines that the play counts of the contentitems A and B in each of the first and fourth time slots are “1” and“0”, respectively.

That is, since only one target content having a total play count of 1 orless, i.e., the content item A or B, is allocated to each time slot, theresult of performing scheduling on the content items A and B may be thesame as the result of determining the order in which to play the contentitems A and B (i.e., A->B->B->A->B->B).

In short, in a case where the play time of target content and the lengthof each time slot are identical, the content scheduling apparatus 100may determine the play order of the target content. In reality, the costof computing may undesirably increase if each time slot is set to beshort. Thus, it may be effective that the play order of target contentis determined according to a predefined policy after determining theplay count of target content in each time slot by setting each time slotto be sufficiently long.

A method in which the content scheduling apparatus 100 determines theweight value of the target content in each time slot based on the targetcontent's preference for each target customer base and the floatingpopulation of each target customer base in each time slot willhereinafter be described with reference to FIGS. 8A through 8C.

In a case where the target customer base of target content consists onlyof a single age group such as 10s or 20s, the content schedulingapparatus 100 may determine the floating population of each targetcustomer in each time slot as the weight value of the target customer ineach time slot. However, in reality, the target customer base of thetarget content is highly likely to include more than one age group, inwhich case, the content scheduling apparatus 100 may determine theweight value of each time slot by calculating a weighted average using aweight value indicating the target content's preference for each agegroup. This will hereinafter be described in detail with reference toFIGS. 8A through 8C.

FIG. 8A shows weight values indicating target customer bases'preferences, FIG. 8B shows the floating populations of the targetcustomer bases of FIG. 8A in each time slot, and FIG. 8C shows weightvalues calculated based on the information shown in FIG. 8A and theinformation shown in FIG. 8B. For convenience, it is assumed that thereare three target customer bases, i.e., 10s, 20s, and 30s age groups, andthree time slots, i.e., “9:00 a.m.”, “3:00 p.m.”, and “9:00 p.m.” timeslots, and that a total floating population is uniform in each of thethree time slots.

Referring to FIG. 8A, weight values of 10, 50, and 40 are acquired bythe content scheduling apparatus 100 and indicate the preferences of the10s, 20s, and 30s age groups, respectively. The preferences of the 10s,20s, and 30s age groups may be acquired from, for example, a contentproviding entity.

Referring to FIG. 8B, the floating populations of the 10s, 20s, and 30sage groups, which are acquired by the content scheduling apparatus 100,show that the 10s, 20s, and 30s age groups have a largest floatingpopulation in the “3:00 p.m.”, “9:00 p.m.”, and “9:00 a.m.” time slots,respectively. As described above, the floating populations of the 10s,20s, and 30s age groups may be acquired from, for example, an imageanalysis module.

Referring to FIG. 8C, the content scheduling apparatus 100 may determinethe weight values of the “9:00 a.m.”, “3:00 p.m.”, and “9:00 p.m.” timeslots by calculating weighted average floating populations of the 10s,20s, and 30s age groups using the weight values of the 10s, 20s, and 30sage groups.

Specifically, the weight value of the “9:00 a.m.” time slot, i.e., 36,may be obtained by multiplying the floating populations of the 10s, 20s,and 30s age groups, i.e., 20, 20, and 60, by the weight values of the10s, 20s, and 30s age groups, i.e., 10, 50, and 40, and adding up theresults of the multiplication, i.e., 2 (=20*0.1), 10 (=20*0.5), and 24(=60*0.4). In this manner, the weight values of the “3:00 p.m.”, and“9:00 p.m.” time slots may be calculated to be 24 and 40, respectively.The content scheduling apparatus 100 may readjust the weight values ofthe “9:00 a.m.”, “3:00 p.m.”, and “9:00 p.m.” time slots, as necessary.

In the method described above with reference to FIGS. 8A through 8C, thecontent scheduling apparatus 100 determines the weight value of targetcontent in each time slot based on the target content's preference foreach target customer base and the floating population of each targetcustomer base. Accordingly, the content scheduling apparatus 100 canrelax any restriction on the type of input data needed for schedulingand can thus perform scheduling using various types of input data.

Various linear programming models that can be generated by the contentscheduling apparatus 100 will hereinafter be described with reference toFIGS. 9A through 13.

First, basic linear programming models that can be generated in S500 ofFIG. 6 will hereinafter be described with reference to FIGS. 9A and 9B.In the description that follows, it is assumed that there are n targetcontent items, a scheduling target period is one day, and the length ofeach time slot is one hour. However, the number of target content items,the length of the scheduling target period, and the length of each timeslot are not particularly limited, but may vary.

Referring to FIG. 9A, X_(ij) denotes a decision variable indicating theplay count of a j-th target content item, which is one of the n targetcontent items, in an i-th time slot. For example, X₁₅ indicates the playcount of a fifth target content item in a first time slot. Since thereare a total of 24 time slots (=24 hr/1 hr), an index i may be an integerbetween 1 and 24, an index j may be an integer between 1 and n, andthere may be a total of 24n decision variables (X_(ij)).

A function that can maximize the sum of the decision variable X_(ij)multiplied by a weight value W_(ij), which indicates the j-th targetcontent item's preference for the i-th time slot, may be set as anobjective function of a linear programming model because the value ofthe objective function becomes greater when target content is allocatedto a time slot having a high preference level than when the targetcontent is allocated to a time slot having a low preference level.

The weight value W_(ij) is the weight value that the j-th target contentitem has in the i-th time slot. The more preferred the j-th targetcontent item is, the greater the weight value W_(ij) becomes.

Alternatively, a function that can minimize the sum of the decisionvariable X_(ij) multiplied by the weight value W_(ij) may be set as anobjective function of a linear programming model according to theduality of linear programming. It is noted that a linear programmingmodel having the function that can minimize the sum of the decisionvariable X_(ij) multiplied by the weight value W_(ij) as its objectivefunction is merely a variation of the linear programming model of FIG.9A according to the duality of linear programming and is thussubstantially the same as the linear programming model of FIG. 9A. Theduality of linear programming is already well known in the art to whichthe present disclosure pertains, and thus, a detailed descriptionthereof will be omitted in order not to obscure the gist of the presentdisclosure. The objective functions and constraints of other linearprogramming models that will be described later may also be variedaccording to the duality of linear programming, but such variations alsofall with the scope of the present disclosure.

The content scheduling apparatus 100 may set one of a first constraintthat the sum of the decision variable X_(ij) is greater than or equal toa total play count N_(j) of the j-th target content item, i.e., i.e.,ΣX_(ij)≥N_(j), and a second constraint that the sum of the decisionvariable X_(ij) is smaller than or equal to the total play count N_(j)of the j-th target content item, i.e., ΣX_(ij)≤N_(j), as a constraint ofa linear programming model.

Specifically, in a case where the total play count of each of the ntarget content items refers to a minimum number of times each of the ntarget content items should be played over a scheduling target period,the content scheduling apparatus 100 may set the first constraint as aconstraint of a linear programming model. On the other hand, in a casewhere the total play count of each of the n target content items refersto a maximum number of times each of the n target content items can beplayed over the scheduling target period, the content schedulingapparatus 100 may set the second constraint as a constraint of a linearprogramming model. For example, if the total play count of each of the ntarget content items is given by a content contract, the contentscheduling apparatus 100 may set the first constraint as a constraint ofa linear programming model because the number of plays of each of the ntarget content items, specified in the content contract, generallyrefers to the minimum number of times each of the n target content itemsshould be played over the scheduling target period. FIG. 9A shows anexample in which the total play count of each of the n target contentitems refers to the minimum number of times each of the n target contentitems should be played over the scheduling target period.

Since the above-mentioned total play count-related constraint such asthe first or second constraint can be set for each of the n targetcontent items, a total of n total play count-related constraints(ΣX_(ij)≤N_(j) where j=1, 2, . . . , n) can be set in a linearprogramming model.

The content scheduling apparatus 100 may set a constraint that the sumof the decision variable X_(ij) multiplied by a play time H_(j) of thej-th target content item is smaller than or equal to 3600 seconds, whichis the length of each time slot, i.e., ΣX_(ij)*H_(j)≤3600, as anadditional constraint of a linear programming model in order for theplay count of each of the n target content items in each time slot,i.e., the decision variable X_(ij), to be within a predetermined rangecorresponding to the length of each time slot. Since the additionalconstraint can be set for each time slot, a total of 24 additionalconstraints (i.e., ΣX_(ij)*H_(j)3≤3600 where i=1, 2, . . . , 24) can beset in a linear programming model.

In the linear programming model, the length of each time slot is set to3600 seconds, which is equal to one hour, in consideration that the playtime H_(j) is measured in seconds.

A non-negative constraint, i.e., X_(ij)≥0, may be set as a finalconstraint of a linear programming model in consideration that thedecision variable X_(ij), which indicates the play count of the j-thtarget content item in the i-th time slot, is always greater than orequal to zero. Since the non-negative constraint is set for each of the24 n decision variables, a total of 24 n final constraints can be set ina linear programming model.

Once a linear programming model is generated, the content schedulingapparatus 100 may determine the play count of each of the n targetcontent items in each time slot by finding the optimal solution orapproximate optimal solution of the linear programming model, i.e., thevalue of the decision variable X_(ij) that maximizes the objectivefunction of FIG. 9A, from among feasible solutions that satisfy theconstraints shown in FIG. 9A.

In a case where the content scheduling method 100 uses the simplexmethod, the linear programming model of FIG. 9A may be converted intostandard form, i.e., a linear programming model shown in FIG. 9B. Thelinear programming model of FIG. 9B is almost the same as the linearprogramming model of FIG. 9A except that its constraints are representedas equations, rather than as inequalities, by using a slack variable (orsurplus variable) S_(ij). That is, the linear programming models ofFIGS. 9A and 9B are substantially identical, even through there aredifferences therebetween. The slack variable (or surplus variable)S_(ij) is already well known in the art to which the present disclosurepertains, and thus, a detailed description thereof will be omitted.

The basic linear programming model that can be generated by the contentscheduling apparatus 100 have been described above with reference toFIGS. 9A and 9B. Linear programming models that can be generated by thecontent scheduling apparatus 100 when a variety of constraints are addedwill hereinafter be described.

First, a linear programming model that can be generated by the contentscheduling apparatus 100 when a constraint regarding the operation timeof the content playing apparatus 300 is added will hereinafter bedescribed with reference to FIG. 10.

The linear programming model of FIG. 9A is a linear programming modelestablished on the assumption that the content playing apparatus 300continues to operate during an entire scheduling target period. However,in reality, the content playing apparatus 300 may not necessarily bedriven throughout the entire scheduling target period. For example, thetime for which the place where the content playing apparatus 300 isinstalled can be accessed may be fixed, or the operation time of thecontent playing apparatus 300 may be restricted according to theoperation policy of the content playing apparatus 300. In this example,the content scheduling apparatus 100 changes the scheduling targetperiod to coincide with the operation time of the content playingapparatus 300, thereby generating a linear programming model similar tothat shown in FIG. 10.

For example, in response to information indicating that the operationtime of the content playing apparatus 300 is 18 hours (from 6:00 to23:00) being acquired, the content scheduling apparatus 100 may changethe length of the scheduling target period to 18 hours, therebyobtaining the linear programming model of FIG. 10.

The linear programming model of FIG. 10 differs from the linearprogramming model of FIG. 9A in the total number of decision variables(X_(ij)), and this is because as the scheduling target period isshortened according to the operation time of the content playingapparatus 300, the number of time slots to be determined by the contentscheduling apparatus 100 is reduced. That is, if the scheduling targetperiod is shortened to 18 hours (from 6:00 to 23:00), a total of 18 timeslots may be generated, and the content scheduling apparatus 100 mayreduce the number of decision variables (X_(ij)) accordingly bycontrolling the range of values of an index i.

A linear programming model that can be generated by the contentscheduling apparatus 100 when a constraint regarding the priority valueof target content is further added will hereinafter be described withreference to FIG. 11.

The linear programming model of FIG. 10 is generated only using theweight value of each target content item in each time slot. The contentscheduling apparatus 100 may additionally use the priority value of eachtarget content item to generate a linear programming model.

The priority value of target content is a value determined based onadditional information not reflected in the weight value of the targetcontent in each time slot. For example, the priority value of targetcontent may be a value determined by the content scheduling apparatus100 based on the contract cost of the target content, determined by acontent providing entity and a content playing entity. If the priorityvalue of target content means a priority value determined according tothe contract cost of the target content, a target content item having ahigh contract cost may have a high priority value.

The priority value of target content may be used to determine prioritiesamong a plurality of target content items having the same weight valuein each time slot or to allocate target content items having a lowweight value in each time slot, but having a high priority value, aheadof other target content items. Accordingly, the priority value of targetcontent may be additionally reflected in the objective function of alinear programming model as a coefficient, as shown in FIG. 11.

Referring to the objective function of the linear programming model ofFIG. 11, symbol {circle around (x)} denotes an arbitrary operator. Thecontent scheduling apparatus 100 may determine the type of the operator{circle around (x)} in consideration of the relationship between theweight value (W_(ij)) of target content in each time slot and thepriority value (R_(j)) of the target content. Alternatively, the type ofthe operator {circle around (x)} may be set in advance based onexperimental results obtained using various operators.

For example, the operator {circle around (x)} may be set as a productoperator. That is, the content scheduling apparatus 100 may set afunction that maximizes the sum of the weight value (W_(ij)) of targetcontent in each time slot, multiplied by the priority value (R_(j)) ofthe target content and a decision variable (X_(ij)) corresponding to thetarget content as the objective function of a linear programming model.In this example, target content can be placed in each time slot if iteither has a large weight value (W_(ij)) in each time slot or has a highpriority value (R_(j)).

In the linear programming model of FIG. 11, it is assumed that thepriority value of target content varies depending on the type of thetarget content, but is uniform in each time slot. Thus, the priorityvalue of target content is represented as R_(j). However, in a casewhere the priority value of target content differs from one time slot toanother time slot, the priority value of the target content may berepresented as R_(ij).

The benefits of reflecting not only the weight value of target contentin each time slot, but also the priority value of the target content, inthe objective function of a linear programming model are as follows.First, if the priority value of the target content is determined by thecontract cost of the target content, the profit of a content playingentity can be further improved because scheduling can be performed infurther consideration of the contract cost of the target content as wellas the weight value of the target content in each time slot. That is,among multiple target content items having similar weight values in eachtime slot, target content items that are paid more are placed ahead ofother content items, and thus, the effect of raising the contract costof target content can be achieved.

A linear programming model that can be generated by the contentscheduling apparatus 100 when a constraint regarding the minimum playcount and/or the maximum play count of target content in each time slotis further added will hereinafter be described with reference to FIG.12.

Referring to FIG. 12, in response to information regarding the minimumplay count and/or the maximum play count of target content in each timeslot being acquired, the content scheduling apparatus 100 may generate anew linear programming model by adding the minimum play count and/or themaximum play count of the target content in each time slot to anexisting linear programming model as a new constraint. For example, in acase where the minimum play count and the maximum play count of thetarget content are 8 and 32, respectively, the content schedulingapparatus 100 may generate a linear programming model shown in FIG. 12.

The linear programming model of FIG. 12 differs from the linearprogramming model of FIG. 11 in that it further has an additionalconstraint regarding the play count (X_(ij)) of target content in eachtime slot, i.e., 8≤X_(ij)≤32. The content scheduling apparatus 100 maycalculate and produce a scheduling result by finding an optimal solutionor approximate optimal solution that can further satisfy the additionalconstraint.

A linear programming model that can be generated by the contentscheduling apparatus 100 when a constraint regarding the play ratio of acertain type of content is further added will hereinafter be describedwith reference to FIG. 13.

In actual use of, for example, an outdoor advertising system, there maybe a constraint, either mandated by a regulation or added according tothe operation policy of the outdoor advertising system, that a certaintype of content such as public-purpose content or advertisement contentfor a particular shopping mall should be played at a certain ratio. Forexample, there is a regulation in the “Enforcement Decree of the Act onthe Management of Outdoor Advertising and the Promotion of the OutdoorAdvertising Industry” that advertisement content for public purposesshould be played within the range of 20/100 per hour.

In response to information regarding the play ratio of a certain type ofcontent being acquired, the content scheduling apparatus 100 maygenerate a new linear programming model by adding the play ratio of thecertain type of content to an existing linear programming model as a newconstraint, and this is because if a constraint regarding the play ratioof the certain type of content is in place, a total scheduling timeavailable for target content is shortened. For example, ifpublic-purpose content is added at a ratio of 20/100 per hour, othertypes of target content can only be scheduled to be played within therange of 48 minutes per hour (=60 min*80/100), and thus, the play ratioof the public-purpose content may be set as a constraint. The contentscheduling apparatus 100 may generate a new linear programming model byadding a constraint that the total play time of each content type isless than or equal to a value obtained by multiplying the play ratio ofeach type of target content by the length of an entire scheduling targetperiod. As described above, when a constraint regarding the operationtime of the content reproduction apparatus 300 is added, the operationtime of the content reproduction apparatus 300 becomes the schedulingtarget period.

The linear programming model of FIG. 13 is a linear programming modelgenerated when the operation time of the content reproduction apparatus300 is 18 hours and the play ratios of public advertisement content,shopping mall advertisement content, and external advertisement contentare 20%, 40%, and 40%, respectively. Referring to FIG. 13, a variable tdenotes the type of content, wherein the variable t indicates publicadvertisement content when having a value of 1, shopping mall-relatedcontent when having a value of 2, and external advertisement contentwhen having a value of 3. ΣΣX_(ij) ³*H_(j) indicates the total play timeof each type of target content, and a variable R¹ indicates the playratio of each type of target content.

Referring to FIG. 13, a play ratio R¹ of public advertisement content is0.2, a play ratio R² of shopping mall advertisement content is 0.4, aplay ratio R³ of external advertisement content is 0.4, and thescheduling target period is 18 hours, which is 3600*18 seconds.

In a case where target content is external advertisement content (i.e.,t=3), the content scheduling apparatus 100 calculates and produces ascheduling result by finding the optimal solution or approximate optimalsolution of a linear programming model having added thereto a constraintregarding the play ratio R³, i.e., ΣΣX_(ij) ³*H_(j)≤3600*18*R³. Also, ina case where the target content is public advertisement content (i.e.,t=1), the content scheduling apparatus 100 calculates and produces ascheduling result by finding the optimal solution or approximate optimalsolution of a linear programming model having added thereto a constraintregarding the play ratio R¹, i.e., ΣΣX_(ij) ¹*H_(j)≤3600*18*R¹.

Various linear programming models that can be generated by the contentscheduling apparatus 100 when a variety of constraints are given havebeen described above with reference to FIGS. 9A through 13. As describedabove, even if a variety of constraints are given, the contentscheduling apparatus 100 can perform scheduling in such a manner thatthe given constraints can be satisfied and the objective function of alinear programming model can be optimized. Also, the content schedulingapparatus 100 can provide a flexible, “fine-grained” scheduling methodbased on various linear programming models.

The inventive concept of the present disclosure, described above withreference with reference to FIGS. 6 through 13, can be implemented ascode on a computer-readable recording medium. The computer-readablerecording medium may be, for example, a removable recording medium, suchas a CD, a DVD, a Blu-ray disc, a USB storage device, or a removablehard disk, or a fixed recording medium, such as a ROM, a RAM, or a harddisk embedded in a computer. A computer program recorded on thecomputer-readable recording medium may be transmitted from one computingdevice to another computing device via a network such as the Internet tobe installed and used in the other computing device.

Although operations are shown in a specific order in the drawings, itshould not be understood that desired results can be obtained when theoperations must be performed in the specific order or sequential orderor when all of the operations must be performed. In certain situations,multitasking and parallel processing may be advantageous. According tothe above-described embodiments, it should not be understood that theseparation of various configurations is necessarily required, and itshould be understood that the described program components and systemsmay generally be integrated together into a single software product orbe packaged into multiple software products.

While the present invention has been particularly illustrated anddescribed with reference to exemplary embodiments thereof, it will beunderstood by those of ordinary skill in the art that various changes inform and detail may be made therein without departing from the spiritand scope of the present invention as defined by the following claims.The exemplary embodiments should be considered in a descriptive senseonly and not for purposes of limitation.

What is claimed is:
 1. A content scheduling method, which is performedby a content scheduling apparatus, comprising: acquiring a total playcount of target content; determining a plurality of weight values of thetarget content with respect to a plurality of time slots, each weightvalue of the plurality of weight values indicating a first preferencefor the target content with respect to each time slot of the pluralityof time slots; generating a linear programming model using the acquiredtotal play count and the plurality of weight values; and determining,via a processor, a play count of the target content in the each timeslot of the plurality of time slots based on the linear programmingmodel.
 2. The content scheduling method of claim 1, wherein the eachweight value of the target content in the each time slot is determinedbased on a total floating population in the each time slot, wherein thetotal floating population represents a number of people who visited orpass by a designated geographical area.
 3. The content scheduling methodof claim 1, wherein the each weight value of the target content in theeach time slot is determined based on (i) a second preference for thetarget content with respect to each target customer base of a pluralityof target customer bases, and (ii) a first floating population of theeach target customer base in the each time slot, wherein the firstfloating population represents a number of people, of the each targetcustomer base in the each time slot, who visited or pass by a designatedgeographical area.
 4. The content scheduling method of claim 3, whereinthe determining the each weight value of the target content comprises:calculating a weighted average of the first floating population of theeach target customer base in the each time slot using the secondpreference for the target content with respect to the each targetcustomer base; and determining the each weight value of the targetcontent in the each time slot based on the calculated weighted average.5. The content scheduling method of claim 3, wherein the first floatingpopulation of the each target customer base in the each time slot isdetermined based on a second floating population in the each time slotand demographic data extracted by a computer vision algorithm.
 6. Thecontent scheduling method of claim 1, wherein the generating the linearprogramming model comprises: setting the play count of the targetcontent in the each time slot as a decision variable of the linearprogramming model; setting an objective function of the linearprogramming model using the each weight value of the target content inthe each time slot and the decision variable; and setting a constraintof the linear programming model using the total play count of the targetcontent and the decision variable.
 7. The content scheduling method ofclaim 6, wherein the objective function of the linear programming modelmaximizes a sum of values obtained by multiplying the plurality ofweight values of the target content in the plurality of time slots bythe decision variable.
 8. The content scheduling method of claim 6,wherein the constraint of the linear programming model requires that asum of the decision variable for the each time slot be greater than orequal to the total play count of the target content.
 9. The contentscheduling method of claim 6, wherein the setting the constraint of thelinear programming model comprises: acquiring a play time of the targetcontent; and setting the constraint such that a sum of values obtainedby multiplying the play time of the target content by the decisionvariable for the each time slot is less than or equal to a length of theeach time slot.
 10. The content scheduling method of claim 1, furthercomprising: determining a priority value of the target content, whereinthe generating the linear programming model comprises: setting the playcount of the target content in the each time slot as a decision variableof the linear programming model; and setting an objective function ofthe linear programming model using the plurality of determined weightvalues and the determined priority value.
 11. The content schedulingmethod of claim 10, wherein the setting the objective function of thelinear programming model comprises setting a function that maximizes asum of values obtained by multiplying the plurality of weight values ofthe target content in the plurality of time slots by the priority valueof the target content and the decision variable for the each time slot.12. The content scheduling method of claim 1, further comprising:acquiring a minimum play count and a maximum play count of the targetcontent in the each time slot, wherein the generating the linearprogramming model comprises: setting the play count of the targetcontent in the each time slot as a decision variable of the linearprogramming model; and setting a constraint of the linear programmingmodel using the acquired minimum play count and the acquired maximumplay account.
 13. The content scheduling method of claim 12, wherein theconstraint requires that the decision variable be greater than or equalto the acquired minimum play count and is less than or equal to theacquired maximum play count.
 14. The content scheduling method of claim1, further comprising: acquiring a play ratio for each type of thetarget content, wherein the generating the linear programming modelcomprises: setting the play count of the target content in the each timeslot as a decision variable of the linear programming model; and settinga constraint of the linear programming model using the acquired playratio.
 15. The content scheduling method of claim 14, wherein, accordingto the constraint of the linear programming model: when the targetcontent is of a first type, a first total play time of first-type targetcontent is less than or equal to a scheduling target period multipliedby a first play ratio of the first-type target content; and when thetarget content is of a second type, a second total play time ofsecond-type target content is less than or equal to the schedulingtarget period multiplied by a second play ratio of the second-typetarget content.
 16. A content scheduling apparatus comprising: at leastone processor; a network interface configured to communicate with acontent playing apparatus; a memory configured to load a computerprogram executed by the at least one processor; and a storage configuredto store the computer program which, when executed by the at least oneprocessor, causes the at least one processor to perform operationscomprising: acquiring a total play count of target content; determininga plurality of weight values of the target content with respect to aplurality of time slots, each weight value of the plurality of weightvalues indicating a preference for the target content with respect toeach time slot of the plurality of time slots; generating a linearprogramming model using the acquired total play count and the pluralityof weight values; and determining a play count of the target content inthe each time slot of the plurality of time slots based on the linearprogramming model.
 17. A computer-readable storage medium storinginstructions which, when executed by a processor, cause the processor toperform operations comprising: acquiring a total play count of targetcontent; determining a plurality of weight values of the target contentin with respect to a plurality of time slots, each weight value of theplurality of weight values indicating a preference for the targetcontent with respect to each time slot of the plurality of time slots;generating a linear programming model using the acquired total playcount and the plurality of weight values; and determining a play countof the target content in the each time slot of the plurality of timeslots based on the linear programming model.